CN104635834B - The experimental provision and method of the photovoltaic maximal power tracing based on immune genetic algorithm - Google Patents

The experimental provision and method of the photovoltaic maximal power tracing based on immune genetic algorithm Download PDF

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CN104635834B
CN104635834B CN201510024195.8A CN201510024195A CN104635834B CN 104635834 B CN104635834 B CN 104635834B CN 201510024195 A CN201510024195 A CN 201510024195A CN 104635834 B CN104635834 B CN 104635834B
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solar panel
antibody
voltage
maximum power
monitoring platform
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CN104635834A (en
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杨锡运
杨国田
刘禾
陆会明
李新利
武晓宁
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North China Electric Power University
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

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Abstract

The invention belongs to photovoltaic generation experimental provision field, more particularly to a kind of photovoltaic maximal power tracing based on immune genetic algorithm experimental provision and method.Experimental provision includes：Solar panel, battery, controller, power device, load, monitoring platform；The voltage of solar panel, electric current, temperature and amount of radiation signal are transmitted to monitoring platform by voltage sensor, current sensor, temperature sensor, radiation amount sensor, by the voltage of the Computer display solar panel on monitoring platform, electric current, power, temperature and amount of radiation；The output end connection controller of voltage sensor and current sensor, controller calculates maximum power point and voltage by adaptive Immunity Genetic Algorithm, output pwm signal is to power device, disturbance voltage is produced by changing the dutycycle of pwm signal, control solar panel output reaches maximum power point.

Description

The experimental provision and method of the photovoltaic maximal power tracing based on immune genetic algorithm
Technical field
It is maximum the invention belongs to photovoltaic generation experimental provision field, more particularly to a kind of photovoltaic based on immune genetic algorithm The experimental provision and method of power tracking.
Background technology
With the finiteness of conventional energy resource and becoming increasingly conspicuous for environmental problem, with the environmentally friendly and renewable new energy as speciality Source increasingly obtains the attention of national governments.Used as a kind of with very big potentiality of new energy, photovoltaic generation is solar energy Using the principal mode of solar energy, have broad application prospects.By setting up experimental provision, disclose photovoltaic generation principle and The course of work, for culture future be engaged in the undergraduate course of power specialty, postgraduate to put into practice technical ability significant.
The output of photovoltaic array is affected by many factors, such as amount of radiation, temperature, inclination angle, and wherein amount of radiation is main Influence factor.Under certain environmental conditions, the output of photovoltaic cell has very strong non-linear, and only one of which is maximum Power output point.In order to improve the generating efficiency of photovoltaic cell, the electric energy of photovoltaic array output is maximally utilised, it is necessary to logical The regulation of overpower device makes photovoltaic array output voltage convergence maximum power point output voltage, to ensure photovoltaic array in maximum work Rate point runs and obtains the maximum energy.
Traditional perturbation observation method is only applicable to shadeless situation, when occurring local shades on photovoltaic array, work( Rate curve of output is changed into multimodal from unimodal, and at this moment perturbation observation method will produce misjudgment phenomenon, it is impossible to trace into maximum power point.
The content of the invention
Regarding to the issue above, it is more adept at searching for the spy of the optimal solution of multi peak value using adaptive Immunity Genetic Algorithm Point, the present invention proposes the experimental provision and method of a kind of photovoltaic maximal power tracing based on immune genetic algorithm.
A kind of experimental provision of the photovoltaic maximal power tracing based on immune genetic algorithm, including：Solar panel, storage Battery, controller, power device, load, monitoring platform；
Wherein, solar panel passes sequentially through controller, power device and is connected with load, battery, monitoring platform point It is not connected with controller, voltage sensor, current sensor, temperature sensor, the amount of radiation biography installed on solar panel Sensor is connected with monitoring platform respectively, and it is flat that the voltage of solar panel, electric current, temperature and amount of radiation signal are transmitted into monitoring Platform, by the voltage of the Computer display solar panel on monitoring platform, electric current, power, temperature and amount of radiation；
The output end connection controller of voltage sensor and current sensor, controller passes through adaptive Immunity Genetic Algorithm Maximum power point and voltage are calculated, output pwm signal to power device is produced by changing the dutycycle of pwm signal Disturbance voltage, control solar panel output reaches maximum power point.
The solar panel is arranged on the bearing of adjustable angle, by the Angulation changes solar-electricity for changing bearing The mounted angle of pond plate.
Support is installed on the solar panel, packaged type shadow shield, solar panel are fixed with support Surrounding is carved with the scale of length dimension, and solar panel shadow surface is calculated by reading the scale corresponding to shadow shield shadow Product.
The packaged type shadow shield includes translucent plastic sheet and black opaque-plastic plate.
Interface in the computer of the monitoring platform uses Labview software programmings.
A kind of experimental technique of the photovoltaic maximal power tracing based on immune genetic algorithm, including：
Step 1, unlatching experimental teaching unit, are sequentially adjusted in the setting angle of solar panel, according to sensor respectively The voltage of the solar panel for collecting, electric current, temperature and Radiance data, data recorded on monitoring platform respectively, Voltage, electric current, temperature and the Radiance data corresponding to solar panel difference mounted angle are drawn out on monitoring platform Curve.
Step 2, be sequentially adjusted in respectively shadow shield cover solar cell plate suqare, according to the solar energy that sensor is collected The voltage of cell panel, electric current, temperature and Radiance data, data recorded on monitoring platform respectively；Painted on monitoring platform Make the corresponding voltage of different shadow shield masked area changes, electric current, temperature and Radiance data curve；
Step 3, controller calculate maximum power point and voltage, output pwm signal by the algorithm of perturbation observation method To power device, disturbance voltage is produced by changing the dutycycle of pwm signal, control solar panel output reaches maximum Power points；
Step 4, controller calculate maximum power point and voltage, output PWM letters by adaptive Immunity Genetic Algorithm Number to power device, disturbance voltage is produced by changing the dutycycle of pwm signal, control solar panel output reaches most High-power point；
Step 5, draw and compare perturbation observation method and maximum power point change that adaptive Immunity Genetic Algorithm is obtained is bent Line.
The step of controller calculates maximum power point by adaptive Immunity Genetic Algorithm includes：
1) antigen recognizing；
2) initial population is produced；
3) fitness is calculated；
4) promotion and suppression of antibody, produces new memory cell；
5) self adaptation is intersected, is made a variation, and produces new parent colony；
6) judge whether to meet end condition, if it is, output result, end loop；If not, turning next step.
7) the new parent colony that will 5) produce and 4) the new memory cell that produces constitute new population, and 3) return re-starts Next time calculates.
The beneficial effects of the present invention are：Voltage, electric current, power, temperature and radiation are intuitively shown by this experimental provision The change of amount, calculates simple, and configuration is convenient, effect is significant, is had using adaptive Immunity Genetic Algorithm tracking maximum power point Good tracking effect, and fast response time, can realize that solar-energy photo-voltaic cell is exported in shadow-free and under having shadowed condition Power is maximized.
Brief description of the drawings
Fig. 1 is based on the experimental provision structure chart of the photovoltaic maximal power tracing of immune genetic algorithm；
Specific embodiment
Below in conjunction with the accompanying drawings, preferred embodiment is elaborated.
A kind of experimental provision of the photovoltaic maximal power tracing based on immune genetic algorithm, as shown in figure 1, including：The sun Can cell panel, battery, controller, power device, load, monitoring platform；
Wherein, solar panel passes sequentially through controller, power device and is connected with load, battery, monitoring platform point It is not connected with controller, voltage sensor, current sensor, temperature sensor, the amount of radiation biography installed on solar panel Sensor is connected with monitoring platform respectively, and it is flat that the voltage of solar panel, electric current, temperature and amount of radiation signal are transmitted into monitoring Platform, by the voltage of the Computer display solar panel on monitoring platform, electric current, power, temperature and amount of radiation；
The output end connection controller of voltage sensor and current sensor, controller passes through adaptive Immunity Genetic Algorithm Maximum power point and voltage are calculated, output pwm signal to power device is produced by changing the dutycycle of pwm signal Disturbance voltage, control solar panel output reaches maximum power point.
The solar panel is arranged on the bearing of adjustable angle, by the Angulation changes solar-electricity for changing bearing The mounted angle of pond plate.
Support is installed on the solar panel, packaged type shadow shield, solar panel are fixed with support Surrounding is carved with the scale of length dimension, and solar panel shadow surface is calculated by reading the scale corresponding to shadow shield shadow Product.
The packaged type shadow shield includes translucent plastic sheet and black opaque-plastic plate.
Interface in the computer of the monitoring platform uses Labview software programmings.
A kind of experimental technique of the photovoltaic maximal power tracing based on immune genetic algorithm, including：
Step 1, unlatching experimental teaching unit, are sequentially adjusted in the setting angle of solar panel, according to sensor respectively The voltage of the solar panel for collecting, electric current, temperature and Radiance data, data recorded on monitoring platform respectively, Voltage, electric current, temperature and the Radiance data corresponding to solar panel difference mounted angle are drawn out on monitoring platform Curve.
Step 2, be sequentially adjusted in respectively shadow shield cover solar cell plate suqare, according to the solar energy that sensor is collected The voltage of cell panel, electric current, temperature and Radiance data, data recorded on monitoring platform respectively；Painted on monitoring platform Make the corresponding voltage of different shadow shield masked area changes, electric current, temperature and Radiance data curve；
Step 3, controller calculate maximum power point and voltage, output pwm signal by the algorithm of perturbation observation method To power device, disturbance voltage is produced by changing the dutycycle of pwm signal, control solar panel output reaches maximum Power points；
Step 4, controller calculate maximum power point and voltage, output PWM letters by adaptive Immunity Genetic Algorithm Number to power device, disturbance voltage is produced by changing the dutycycle of pwm signal, control solar panel output reaches most High-power point；
Step 5, draw and compare perturbation observation method and maximum power point change that adaptive Immunity Genetic Algorithm is obtained is bent Line.
The step of controller calculates maximum power point by adaptive Immunity Genetic Algorithm includes：
When the power output rate of change of cell panel is more than 0.1, start immune response,
1) antigen recognizing；
It is input antigen with the shadow condition of solar panel,
2) initial population is produced；
Antibody is defined as object photovoltaic array voltage to be optimized, M memory cell in initial data base by with Machine is produced, and N number of different antibody is randomly generated in decision space, and above-mentioned N number of different antibody and M memory cell are constituted Initial population.
3) fitness is calculated；
The purpose of this algorithm is to obtain maximum power tracing of the solar panel under local shades, therefore by solar energy The power output of cell panel is configured to the fitness function A between antibody and antigenv。
4) promotion and suppression of antibody, produces new memory cell；
The promotion and suppression of antagonist are, with the expectation breeding potential P of antibody as standard, the expectation of antibody to be calculated first Breeding potential：
The P of each antibody is by the fitness A between antibody and antigenvWith AC CvTwo parts are together decided on, i.e.,
Wherein, α is constant.
AC CvRatio shared by similar antibodies in colony
Wherein, N is antibody sum；SV,SIt is the similarity between antibody V and antibody S, calculating process is as follows, uses first Initial similarity between the R continuation method calculating antibody V and antibody S of deformation is TV,S
Wherein kV, sIt is identical digit in antibody V and antibody S；L is the length of antibody.
Work as Tv, when s is more than a threshold value 0.9 set in advance, then define Sv, s=1, otherwise Sv, s=0.
Then, colony is carried out into descending arrangement by expectation breeding potential P, takes top n antibody and constitute new initial parent colony, Preceding M antibody is taken to be stored in memory cell storehouse.
5) self-adaptive cross operation, produces new parent colony；
Crossover probability P in adaptive Immunity Genetic AlgorithmcWith mutation probability PmAlgorithm is enabled to keep colony various Property while, ensure convergence.Pc、PmCalculating formula be：
In formula, fmaxIt is maximum adaptation degree in colony；favgIt is the average fitness in per generation colony；F ' is to be intersected two Larger fitness value in individuality；F is the individual fitness value that to make a variation.P is setc1=0.9, Pc2=0.6, Pm1=0.1, Pm2=0.01.
Using above-mentioned result of calculation, self adaptation intersection, mutation operation are carried out to new initial parent colony, produce new father For colony.
6) judge whether to meet end condition, if it is, output result, end loop；If not, turning next step.
The end condition of immune response is that fitness function change is less than 0.0001, i.e., optimal antibody no longer changes, Then think to have found maximum power point, immune response terminates.
7) the new parent colony that will 5) produce and 4) the new memory cell that produces constitute new population, and 3) return re-starts Next time calculates.
When the change of the fitness function of system is more than 0.0001, show that the power output of system, also in change, is immunized Algorithm finds optimal antibody, therefore the calculating of subsequent cycle should be carried out using new population.
According to said process, the optimal value for antibody that the system can be searched out according to adaptive Immunity Genetic Algorithm：I.e. The optimal voltage of solar panel, the peak power of solar cell is realized by changing solar panel both end voltage Output.
Monitoring platform then can intuitively show voltage, electric current, power, temperature and the amount of radiation of solar panel in real time, The situation of change of above-mentioned value during observable solar panel change of pitch angle.
Table 1 describes the system maximal power tracing result under 3 kinds of different local shades obstruction conditions.Experimental condition is such as Under：Two pieces of solar panels of same material, area and performance are taken, it is rack-mount with identical mounted angle, one piece Maximum power tracing is carried out using adaptive Immunity Genetic Algorithm, another piece carries out peak power and chase after using conventional perturbation observation method Track, under 3 kinds of different shadowed conditions carrying out experiment is carried out.The corresponding experimental condition of shadowed condition 1 is：This two pieces of solar-electricities Pond plate is unobstructed.The corresponding experiment condition of shadow condition 2 is：The 1/3 of solar panel is sheltered from alpha-mask plate Area, the corresponding experiment condition of shadow condition 3 is：1/3 area of solar panel is sheltered from opaque shutter.
From table 1 it follows that in the case where not blocking, conventional perturbation observation method and adaptive Immunity Genetic Algorithm Peak power can be tracked.In the case where local shades are blocked, using adaptive Immunity Genetic Algorithm of the invention with After track maximum power point, the power output of solar panel increases 10%-20% than traditional perturbation observation method.
Tracking result under the different shadowed conditions of table 1
The above, the only present invention preferably specific embodiment, but protection scope of the present invention is not limited thereto, Any one skilled in the art the invention discloses technical scope in, the change or replacement that can be readily occurred in, Should all be included within the scope of the present invention.Therefore, protection scope of the present invention should be with scope of the claims It is defined.

Claims (1)

1. a kind of experimental provision of the photovoltaic maximal power tracing based on immune genetic algorithm, it is characterised in that including：Solar energy Cell panel, battery, controller, power device, load, monitoring platform；
Wherein, solar panel pass sequentially through controller, power device with load be connected, battery, monitoring platform respectively and Controller is connected, voltage sensor, current sensor, temperature sensor, the radiation amount sensor installed on solar panel It is connected with monitoring platform respectively, the voltage of solar panel, electric current, temperature and amount of radiation signal is transmitted to monitoring platform, leads to The voltage of the Computer display solar panel crossed on monitoring platform, electric current, power, temperature and amount of radiation；
The output end connection controller of voltage sensor and current sensor, controller is calculated by adaptive Immunity Genetic Algorithm Go out the voltage of maximum power point and the corresponding solar panel of maximum power point, output pwm signal passes through to power device Change the dutycycle of pwm signal to produce disturbance voltage, control solar panel output reaches maximum power point；
The solar panel is arranged on the bearing of adjustable angle, by the Angulation changes solar panel for changing bearing Mounted angle；
Support is installed on the solar panel, packaged type shadow shield, solar panel surrounding are fixed with support The scale of length dimension is carved with, solar panel irradiated area is calculated by reading the scale corresponding to shadow shield shadow；
The packaged type shadow shield includes translucent plastic sheet and black opaque-plastic plate；
Interface in the computer of the monitoring platform uses Labview software programmings；
The experimental technique of the experimental provision, including：
Step 1, unlatching experimental provision, are sequentially adjusted in the mounted angle of solar panel respectively, are collected according to sensor The voltage of solar panel, electric current, temperature and Radiance data, data recorded on monitoring platform respectively, flat in monitoring Voltage, electric current, temperature and the Radiance data curve corresponding to solar panel difference mounted angle are drawn out on platform；
Step 2, be sequentially adjusted in respectively shadow shield cover solar cell plate suqare, according to the solar cell that sensor is collected The voltage of plate, electric current, temperature and Radiance data, data recorded on monitoring platform respectively；Drawn out on monitoring platform The corresponding voltage of different shadow shield masked area changes, electric current, temperature and Radiance data curve；
Step 3, controller calculate maximum power point and the corresponding sun of maximum power point by the algorithm of perturbation observation method The voltage of energy cell panel, output pwm signal to power device produces disturbance voltage by changing the dutycycle of pwm signal, controls Solar panel output processed reaches maximum power point；
Step 4, controller calculate maximum power point by adaptive Immunity Genetic Algorithm and maximum power point is corresponding too The voltage of positive energy cell panel, output pwm signal to power device produces disturbance voltage by changing the dutycycle of pwm signal, Control solar panel output reaches maximum power point；
Step 5, draw and compare perturbation observation method and adaptive Immunity Genetic Algorithm obtain maximum power point change curve；
The step of controller calculates maximum power point by adaptive Immunity Genetic Algorithm includes：
1) antigen recognizing；It is input antigen with the shadow condition of solar panel；
2) initial population is produced；Antibody is defined as the voltage of object solar panel to be optimized, the M in initial data base Individual memory cell randomly generates N number of different antibody by randomly generating in decision space, above-mentioned N number of different antibody With M memory cell composition initial population；
3) fitness is calculated；The power output of solar panel is configured to the fitness function A between antibody and antigen；
4) promotion and suppression of antibody, produces new memory cell；The promotion and suppression of antagonist are bred with the expectation of antibody Rate is standard, and the expectation breeding potential P of antibody is calculated first：
The expectation breeding potential P of antibody VvBy the fitness function A between antibody V and antigenvWith antibody V concentration CsvTwo parts are determined jointly It is fixed, i.e.,
P v = α A v Σ i = 1 N A i + ( 1 - α ) C v Σ i = 1 N C i - - - ( 1 )
Wherein, α is constant；
Antibody V concentration CsvRatio shared by similar antibodies in colony
C v = 1 N Σ s = 1 N S V , S - - - ( 2 )
Wherein, N is antibody sum；SV,SIt is the similarity between antibody V and antibody S, calculating process is as follows, first using deformation R continuation method calculating antibody V and antibody S between initial similarity be TV,S
T V , S = K V , S L - - - ( 3 )
Wherein KV,SIt is identical digit in antibody V and antibody S；L is the length of antibody；
Work as TV,SDuring more than a threshold value 0.9 set in advance, then S is definedV,S=1, otherwise SV,S=0；
Then, colony is carried out into descending arrangement by expectation breeding potential P, takes top n antibody and constitute new initial parent colony, before taking M antibody is stored in memory cell storehouse；
5) self adaptation is intersected, is made a variation, and produces new parent colony；Crossover probability P in adaptive Immunity Genetic AlgorithmcAnd variation Probability PmEnable to algorithm keep population diversity while, it is ensured that convergence；Pc、PmCalculating formula be：
P c = P c 1 - ( P c 1 - P c 2 ) ( f &prime; - f a v g ) f m a x - f a v g , f &prime; &GreaterEqual; f a v g P c 1 , f &prime; < f a v g - - - ( 4 )
P m = P m 1 - ( P m 1 - P m 2 ) ( f m a x - f ) f m a x - f a v g , f &GreaterEqual; f a v g P m 1 , f < f a v g - - - ( 5 )
In formula, fmaxIt is maximum adaptation degree in colony；favgIt is the average fitness in per generation colony；F ' is to be intersected two Larger fitness value in body；F is the individual fitness value that to make a variation；P is setc1=0.9, Pc2=0.6, Pm1=0.1, Pm2= 0.01；
Using above-mentioned result of calculation, self adaptation intersection, mutation operation are carried out to new initial parent colony, produce new parent group Body；
6) judge whether to meet the end condition of immune response, if it is, output result, end loop；If not, turning next Step；The end condition of immune response is that fitness function A changes are less than 0.0001, i.e., optimal antibody no longer changes, then recognize To have found maximum power point, immune response terminates；
7) the new parent colony that will 5) produce and 4) the new memory cell that produces constitute new population, and 3) return re-starts next time Calculate；When the change of fitness function A is more than 0.0001, show that the power output of solar panel, also in change, is immunized Genetic algorithm does not find optimal antibody, therefore the calculating of subsequent cycle should be carried out using new population.
CN201510024195.8A 2015-01-16 2015-01-16 The experimental provision and method of the photovoltaic maximal power tracing based on immune genetic algorithm Expired - Fee Related CN104635834B (en)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101635540A (en) * 2009-08-18 2010-01-27 河海大学 Device and method for tracking photovoltaic maximum power point
CN102157099A (en) * 2011-04-11 2011-08-17 南京康尼科技实业有限公司 Photovoltaic power generation teaching training system
CN102594211A (en) * 2012-01-19 2012-07-18 北京工商大学 Optimizing method and tracking device for output power of partially shielded photovoltaic power generation system
CN102651087A (en) * 2011-08-30 2012-08-29 广西南宁华泰德隆资讯科技有限公司 Maximum power point-tracking photovoltaic system based on ant colony-artificial immune hybrid optimization algorithm
CN103529899A (en) * 2013-10-17 2014-01-22 深圳市禾望电气有限公司 MPPT controller, power tracking device, photovoltaic power generation and energy storage system

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8716999B2 (en) * 2011-02-10 2014-05-06 Draker, Inc. Dynamic frequency and pulse-width modulation of dual-mode switching power controllers in photovoltaic arrays

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101635540A (en) * 2009-08-18 2010-01-27 河海大学 Device and method for tracking photovoltaic maximum power point
CN102157099A (en) * 2011-04-11 2011-08-17 南京康尼科技实业有限公司 Photovoltaic power generation teaching training system
CN102651087A (en) * 2011-08-30 2012-08-29 广西南宁华泰德隆资讯科技有限公司 Maximum power point-tracking photovoltaic system based on ant colony-artificial immune hybrid optimization algorithm
CN102594211A (en) * 2012-01-19 2012-07-18 北京工商大学 Optimizing method and tracking device for output power of partially shielded photovoltaic power generation system
CN103529899A (en) * 2013-10-17 2014-01-22 深圳市禾望电气有限公司 MPPT controller, power tracking device, photovoltaic power generation and energy storage system

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